Cancers are complex dynamical systems involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Researchers must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. Most of the current tools rely on statistical correlation methods. A toolbox of complex systems approaches has been used for reconstructing cancer networks, interpreting causal relationships in their time-series gene expression patterns, and assisting clinical decision-making in computational oncology.
|Algorithm or Technique|
|Lyapunov exponents (λL)||Network science|
|Frequency spectra||Convergent cross mapping (CCM)|
|Master equation||Waddington landscape reconstruction|
|Boolean networks||Deep learning neural networks|
|Reaction-diffusion equations||Recurrent neural networks (RNNs)|
|Computational simulations||Kolmogorov complexity, K(s)|
Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Alternative computational methods have been developed to analyze large genomic data sets based on cellular network topology, which consists of information of collective interactions between multiple components, such as genes and proteins, in an integrated manner. Compared to genomics analysis based on individual genomic alteration, the network topology-based approach is proven more effective to predict drug response (i.e., phenotype) from the genotypes, as well as classify and cluster cancer subtypes.
CD ComputaBio has developed a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combinations. The results can reveal network-specific drug targets that maximize signaling network-mediated cell response, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes. Contact us now for more service details.